Building a Spam Filter with Python: Using ML to Combat Spam

This is a basic web application that uses a Naive Bayes Classifier to detect spam messages. It uses a pre-existing dataset of labeled messages, trains a model, and uses it to predict whether a given message is spam or not.

Technologies Used

• Flask (web framework)
• Scikit-Learn (machine learning library)
• Pandas (data manipulation library)
• NumPy (numerical computing library)
• Jinja2 (template engine)

How to Run

1. Clone the repository
2. Set up a virtual environment by running python -m venv env (on Windows) or python3 -m venv env (on Linux and macOS)
3. Activate the virtual environment by running env\Scripts\activate (on Windows) or source env/bin/activate (on Linux and macOS)
4. Install the required packages by running pip install -r requirements.txt
5. Run the application by running python spam-classifier.py
6. Open a web browser and navigate to http://localhost:5000

How it Works

1. The application reads a pre-existing dataset of labeled messages from a CSV file.
2. It trains a Naive Bayes Classifier using the dataset.
3. It uses the trained model to predict whether a given message is spam or not.
4. The application displays the prediction result on the web page.

Features

• Detects spam messages using a Naive Bayes Classifier
• Displays the prediction result on the web page
• Allows users to input a message and get a prediction

Limitations

• The application is not perfect and may make mistakes
• The application does not store any data and does not have any user authentication
• The application is not optimized for performance

License

This project is licensed under the MIT License. See the LICENSE file for details.

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